Pair-Associate learning with modulated spike-time dependent plasticity

Authors:
Nooraini Yusoff;André Grüning;Scott Notley
Affiliations:
Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, UK;Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, UK;Department of Computing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, UK
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

Citing 4
Cited 0

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Abstract

We propose an associative learning model using reward modulated spike-time dependent plasticity in reinforcement learning paradigm. The task of learning is to associate a stimulus pair, known as the predictor−choice pair, to a target response. In our model, a generic architecture of neural network has been used, with minimal assumption about the network dynamics. We demonstrate that stimulus-stimulus-response association can be implemented in a stochastic way within a noisy setting. The network has rich dynamics resulting from its recurrent connectivity and background activity. The algorithm can learn temporal sequence detection and solve temporal XOR problem.